KadenFlorsheimDavidLüdeke_Final

SAME-SEX AND RACE EQUITY ANALYSIS

USA

#There were a few outlyers that were filtered out to maintain a more observable color gradient. Palm Springs was the largest and had 17.62% same-sex couples.

USA

Individual in same-sex relationship personal income by race

Individual in opposite-sex relationship personal income by race

Texas

Individual in same-sex relationship personal income by race

Individual in opposite-sex relationship personal income by race

California

#There were a few outlyers that were filtered out to maintain a more observable color gradient. Palm Springs was the largest and had 160 same-sex couples. Why are there odd numbers?

Individual in same-sex relationship personal income by race

Individual in opposite-sex relationship personal income by race

EQUITY ANALYSIS BETWEEN SAME-SEX COUPLES AND OPPOSITE-SEX COUPLES DISAGGREGATED BY RACE

White Alone

# Black or African American alone

Asian American Alone

American Indian alone

Two or More Races

Same-Sex gender Equity Analysis

{r} # census_race_categories <- # c( # "White alone", # "Black or African American alone", # "American Indian alone", # "Alaska Native alone", # "American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races", # "Asian alone", # "Native Hawaiian and Other Pacific Islander alone", # "Some Other Race alone", # "Two or More Races") #

{r} # ca_income_race <- # 1:9 %>% # map_dfr(function(x){ # race <- cleaned %>% # mutate( # RAC1P = as.numeric(RAC1P) # ) %>% # filter( # (RAC1P %in% census_race_categories[x])) %>% # mutate( # PWGTP = as.numeric(PWGTP), # HINCP = as.numeric(HINCP), # Same_sex_household = ifelse( # (CPLT == 2)|(CPLT == 4), # "Same sex couple", # "Opposite sex couple" # ), # income = ifelse( # (HINCP <= 10000), # "$10,000 or less", # HINCP # ), # income = ifelse( # (HINCP > 10000 & HINCP <= 20000), # "$10,001 to $20000", # income # ), # income = ifelse( # (HINCP > 20000 & HINCP <= 30000), # "$20,001 to $30,000", # income # ), # income = ifelse( # (HINCP > 30000 & HINCP <= 40000), # "$30,001 to $40,000", # income # ), # income = ifelse( # (HINCP > 40000 & HINCP <= 50000), # "$40,001 to $50,000", # income # ), # income = ifelse( # (HINCP > 50000 & HINCP <= 75000), # "$50,001 to $75,000", # income # ), # income = ifelse( # (HINCP > 75000 & HINCP <= 100000), # "$75,001 to $100,000", # income # ), # income = ifelse( # (HINCP > 100000 & HINCP <= 150000), # "$100,001 to $150,000", # income # ), # income = ifelse( # (HINCP > 150000 & HINCP <= 200000), # "$150,001 to $200,000", # income # ), # income = ifelse( # (HINCP > 200000), # "$200,001 or more", # income # )) %>% # group_by(income, Same_sex_household) %>% # summarize(estimate = sum(PWGTP)) %>% # ggplot() + # geom_bar( # aes( # x = income %>% factor(mixedsort(order)), # y = estimate, # fill = Same_sex_household # ), # stat = "identity", # position = "stack" # ) + # labs( # x = "Household income", # y = "Number of households", # title = "Bay Area household income by couple type", # fill = "Couple Type" # ) + # coord_flip() + # theme( # legend.position = "bottom", # legend.direction = "vertical" # ) + # guides( # fill = guide_legend( # reverse = T # ) # ) # }) # #

getCensus(

name = “acs/acs1/pums”,

vintage = 2019,

region = “public use microdata area:*“,

regionin = “state:06”,

vars = c(

“SERIALNO”, #Unique ID for each household

“SPORDER”, #Person number

“PWGTP”, #Total number of people

“WGTP”, #Housing Weight

“HINCP”, #Household Income

“CPLT”, #Couple Type

“FINCP”, #Family Income

“PINCP”, #Total Persons Income

“RAC1P” #Recorded Detailed Race Code

)

) %>%

select(!c(GEO_ID,state,NAME) & !ends_with(c(“EA”,“MA”,“M”))) %>%

pivot_longer(

ends_with(“E”),

names_to = “name”,

values_to = “estimate”

) %>%

left_join(

ca_pums %>%

select(name, label)

) %>%

select(-name) %>%

separate(

label,

into = c(NA,NA,“income”),

sep = “!!”

) %>%

filter(!is.na(income)) %>%

mutate(race = census_race_categories[x])

})

# # # # # # # # # # # # # (HINCP < 10000), # "Less than 10,000", # (HINCP > 10000), # "More than 10,000", # 0 # )) # # # # # # IR = ifelse( # # SERIALNO == SERIALNO|RAC1P!=RAC1P, # # 1, # # 0 # # ) # # ) # # # # # # # IRC_compare <- #Attempting to pull out Interracial couples into new data set so we can compare races # # ca_pums_IRC %>% # # mutate( # # PWGTP = as.numeric(PWGTP), # # SERIALNO = as.numeric(SERIALNO)) %>% # # if(SERIALNO == SERIALNO) { # # mutate( # # PWGTP = as.numeric(PWGTP), # # Same_sex_household = ifelse( # # (CPLT == 2)|(CPLT == 4), # # # # 1, # # 0 # # )) # # } # ##Can't figure out how to separate interracial couples--Trying to do if statement # #if {same serialno but different races put them in this data set or create new column} #

{r} # ca_pums_IRC %>% # group_by(HINCP, Same_sex_household) %>% # summarize(estimate = sum(PWGTP)) %>% # ggplot() + # geom_bar( # aes( # x = HINCP, # y = estimate, # fill = Same_sex_household # ), # stat = "identity", # position = "stack" # ) + # labs( # x = "Household income", # y = "Number of households", # title = "Bay Area household income by couple type", # fill = "Couple Type" # ) #

{r} # census_race_categories <- data.frame( # code = c(1,2,3,4,5,6,7,8,9), # category = # c( # "White alone", # "Black or African American alone", # "American Indian alone", # "Alaska Native alone", # "American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races", # "Asian alone", # "Native Hawaiian and Other Pacific Islander alone", # "Some Other Race alone", # "Two or More Races" # )) #

{r} # census_race_categories <- # c( # "White alone", # "Black or African American alone", # "American Indian alone", # "Alaska Native alone", # "American Indian and Alaska Native tribes specified; or American Indian or Alaska Native, not specified and no other races", # "Asian alone", # "Native Hawaiian and Other Pacific Islander alone", # "Some Other Race alone", # "Two or More Races") #

{r} # ca_income_race <- # 1:9 %>% # map_dfr(function(x){ # getCensus( # name = "acs/acs1/pums", # vintage = 2019, # region = "public use microdata area:*", # regionin = "state:06", # vars = c( # "SERIALNO", #Unique ID for each household # "SPORDER", #Person number # "PWGTP", #Total number of people # "WGTP", #Housing Weight # "HINCP", #Household Income # "CPLT", #Couple Type # "FINCP", #Family Income # "PINCP", #Total Persons Income # "RAC1P" #Recorded Detailed Race Code # ) # ) %>% # select(!c(GEO_ID,state,NAME) & !ends_with(c("EA","MA","M"))) %>% # pivot_longer( # ends_with("E"), # names_to = "name", # values_to = "estimate" # ) %>% # left_join( # ca_pums %>% # select(name, label) # ) %>% # select(-name) %>% # separate( # label, # into = c(NA,NA,"income"), # sep = "!!" # ) %>% # filter(!is.na(income)) %>% # mutate(race = census_race_categories[x]) # }) #

{r} # ca_cup <- # getCensus( # name = "acs/acs1/pums", # vintage = 2019, # region = "public use microdata area:*", # regionin = "state:06", # vars = c( # "SERIALNO", #Unique ID for each household # "SPORDER", #Person number # "PWGTP", #Total number of people # "WGTP", #Housing Weight # "HINCP", #Household Income # "CPLT", #Couple Type # "FINCP", #Family Income # "PINCP", #Total Persons Income # "RAC1P") #Recorded Detailed Race Code # ) %>% # left_join( # ca_pums_IRC %>% # select(SERIALNO, public_use_microdata_area) # ) # #

%>%

select(-name) %>%

separate(

label,

into = c(NA,NA,“income”),

sep = “!!”

) %>%

filter(!is.na(income))

# # , # White_Low_Income = ifelse( # (RAC1P == 1) & (HINCP > 100000), # 1, # 0 # ), # Not_White_Low_Income = ifelse( # (RAC1P != 1) & (HINCP > 100000), # 1, # 0)) #

{r} # bay_pums_couple <- # cleaned %>% # Same_sex_household = ifelse( # (CPLT == 2)|(CPLT == 4), # WGTP, # 0) # # %>% # # group_by(PUMA) %>% # # summarize( # # PERSON1 = # # sum(Person1, na.rm =T) # # ) #

# #{r}

bay_pums_couple <-

cleaned %>%

mutate(

WGTP = as.numeric(WGTP),

SPORDER = as.numeric(SPORDER),

partner1 = ifelse(

(SPORDER == 1),

WGTP,

0

),

partner2 = ifelse(

(SPORDER == 2),

WGTP,

0

) %>%

group_by(PUMA) %>%

summarize(

partner1 =

sum(partner1, na.rm =T),

partner2 =

sum(partner2, na.rm =T))

)

# # # # # , # Same_sex_household = ifelse( # (HINCP < 100000) & # (CPLT == 2)|(CPLT == 4), # # WGTP, # 0 # ), # Opposite_sex_household = ifelse( # (HINCP < 100000) & # (CPLT == 1)|(CPLT == 3), # WGTP, # 0 # ), # White = ifelse( # (SERIALNO == 1) & # (RAC1P == 1), # WGTP, # 0 # ) # # # ) %>% # group_by(PUMA) %>% # summarize( # sum_same_sex_household = # sum(Same_sex_household, na.rm =T), # sum_opposite_sex_household = # sum(Opposite_sex_household, na.rm =T), # partner1 = # sum(partner1, na.rm =T), # partner2 = # sum(partner2, na.rm =T)%>% # left_join( # bay_pumas %>% # select(PUMACE10), # by = c("PUMA" = "PUMACE10") # ) %>% # st_as_sf() # ) #

{r} # bay_pums_couple <- # cleaned %>% # mutate( # WGTP = as.numeric(WGTP), # partner1 = ifelse( # (SPORDER == 1), # WGTP, # 0 # ), # partner2 = ifelse( # (SPORDER == 2), # WGTP, # 0 # ), # Same_sex_household = ifelse( # (HINCP < 100000) & # (CPLT == 2)|(CPLT == 4), # # WGTP, # 0 # ), # Opposite_sex_household = ifelse( # (HINCP < 100000) & # (CPLT == 1)|(CPLT == 3), # WGTP, # 0 # ), # White = ifelse( # (SERIALNO == 1) & # (RAC1P == 1), # WGTP, # 0 # ) # # # # # #

{r} # acs_vars_2019_5yr <- # listCensusMetadata( # name = "2019/acs/acs5", # type = "variables" # ) #

{r} # veteran <- # getCensus( # name = "acs/acs5", # vintage = 2019, # region = "county:055", # regionin = "state:06", # vars = "group(S2101)" # ) %>% # select(!c(GEO_ID,state,NAME,county) & !ends_with(c("EA","MA","M"))) %>% # pivot_longer( # ends_with("E"), # names_to = "name", # values_to = "estimate" # ) %>% # left_join( # acs_vars_2019_5yr %>% # select(name, label) # ) #

##How would we filter out just first two people to determine their races? Is sample size too small?

##Veterans, Jobs, Race< which war, income